Get reliable rangeland science

Creating defensible and ecologically relevant quantitative management benchmarks is critical to successful adaptive management. Quantitative management benchmarks can be tiered directly to applicable rangeland health standards to facilitate using quantitative data in decision making. The availability of robust datasets from national monitoring programs such as the Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program, the Landscape Monitoring Framework, and National Resources Inventory present new opportunities to incorporate quantitative, statically valid data into the BLM�s land health evaluation process. However, in this era of �big data,� understanding the context of quantitative indicators is crucial. The BLM�s Eagle Lake Field Office (ELFO) has developed a method for identifying explicit and quantitative management benchmarks that are tied directly to rangeland health standards and informed by ecological site potential, habitat objectives, and the best available literature. From the Standards for Rangeland Health, applicable quantitative terrestrial indicators were attributed to the upland soils and biodiversity standards. These quantitative indicator benchmarks vary by ecological site potential. Within the field office, 8 terrestrial ecological potential �ecoclusters� were identified by analyzing SSURGO together with local and adjacent ecological site concepts where applicable. For indicators that were not addressed in ecological site descriptions, benchmarks were derived from policy, peer-reviewed literature, and relevant baseline monitoring data. Through the cooperation of the interdisciplinary team, management benchmarks were then decided on and the rational was documented. The monitoring data at each site were then evaluated against the appropriate benchmarks to determine if the land health standard was met for that particular indicator. The ELFO can then use a preponderance of evidence approach across indicators to determine if that standard is met overall. This collaborative and iterative process represents a starting framework for rationally developing quantitative management benchmarks and documenting the monitoring data evaluation process in a land health evaluation.